A master thesis from Aalborg University
Projecting Spatial Population Distribution Using a Convolutional Neural Network
Author(s)
Term
4. semester
Publication year
2018
Submitted on
2018-06-07
Pages
121 pages
Abstract
Being able to project future spatial population distributions is an important tool to tackle challenges following continuous global population growth and possible future climate challenges. This report presents an answer to how a convolutional neural network can be used to project future spatial population distribution and what results can be achieved by using this approach. This is investigated by designing a convolutional neural network, PopNet. PopNet identifies complex spatial patterns in historical data, on a 250 meter resolution grid and projects the future spatial population distribution. From this architecture, two future scenarios are simulated based on IIASA SSP2 population projections for Denmark and France respectively and the results evaluated. While the neural network method does have flaws, the results prove how, and that, convolutional neural networks can be used to project future population distribution. A number of key challenges, strengths and weaknesses are found and further alterations are proposed that could improve PopNet precision and applicability for future use.
Documents
Colophon: This page is part of the AAU Student Projects portal, which is run by Aalborg University. Here, you can find and download publicly available bachelor's theses and master's projects from across the university dating from 2008 onwards. Student projects from before 2008 are available in printed form at Aalborg University Library.
If you have any questions about AAU Student Projects or the research registration, dissemination and analysis at Aalborg University, please feel free to contact the VBN team. You can also find more information in the AAU Student Projects FAQs.